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| "paper_id": "2020", | |
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| "date_generated": "2023-01-19T13:05:06.749987Z" | |
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| "title": "TermEval Shared Task Organizers", | |
| "authors": [ | |
| { | |
| "first": "B\u00e9atrice", | |
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| "last": "Daille", | |
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| "first": "Ayla", | |
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| "Rigouts" | |
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| "first": "Patrick", | |
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| "last": "Drouin", | |
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| "first": "Natalia", | |
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| "first": "Philippe", | |
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| { | |
| "first": "Thomas", | |
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| "last": "Lavergne", | |
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| "first": "Ruslan", | |
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| "first": "Carlos", | |
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| "last": "Peri\u00f1\u00e1n-Pascual", | |
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| "first": "Koichi", | |
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| "last": "Takeuchi", | |
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| "abstract": [], | |
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| "text": "The aim of this sixth Computerm workshop is to bring together Natural Language Processing (NLP) and Human Language Technology researchers as well as terminology researchers and practitioners to discuss recent advances in computational terminology and its impact within automatic and human applications. This time we will also host a special session for the shared task TermEval, which uses the large manually annotated ACTER (Annotated Corpora for Term Extraction Research) dataset that covers multiple domains and languages.", | |
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| "text": "(NLP)", | |
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| "section": "Introduction", | |
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| "text": "Terminology has a unique status in language and communication. Theoretically, it is situated within the tension between the flexibility of natural language and the rigidity of artificial sign systems. Reflecting this theoretical status, terms are treated in a specific way in human language practice. In the technical translation pipeline, terms are not \"translated\" but relevant target language terms are looked up and used, as \"mistranslation\" can cause grave consequences. Many organisations, including such public institutions as the EU, WIPO and the NLM, and private LSPs, construct and maintain terminologies. Translation quality assurance schemes identify terminology-related issues as one of the focal checking points. Terminologies also provide important resources for education and knowledge transfer.", | |
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| "text": "Although a substantial number of terms are linguistically categorized as so-called multiword expressions, the requirements and desiderata for handling terms as well as their status in language practice pipelines are different from most other multiword expressions such as idioms.", | |
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| "text": "Computational terminology, if it is to make an in vivo contribution to the human communication ecosystem, needs to take into account this uniqueness of terminology at every stage of research, from defining problems to be solved and determining methods to be adopted, to developing evaluation schemes to be used.", | |
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| "section": "Introduction", | |
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| "text": "In the four years since the 5th Workshop on Computational Terminology (Computerm 2016) was held, advancements in distributional representations and deep learning have changed, at least on the surface, the major NLP scene. What about terminology processing? This issue has yet to be fully explored or discussed. For instance, while Neural Machine Translation (NMT) has greatly improved target language fluency, it is sometimes reported that the quality of NMT is on a par with Statistical Machine Translation when it comes to the translation of terminology. Given the unique status of technical terms in communication and language practice, there is much for computational terminology to examine and explore in the face of the recent development of deep learning based NLP technologies, which may not necessarily be in the same line with most NLP tasks.", | |
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| "text": "This workshop thus aims to investigate what deep learning has brought to computational terminology, its impact within human applications, and the new questions that it raises within the scope of terminology. With this in mind, Prof. Dr. Sabine Schulte im Walde (University of Stuttgart) was invited to highlight the new results achieved in modelling noun compound meaning in general and domain-specific language using such statistical methods.", | |
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| "section": "Introduction", | |
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| "text": "We received 20 submissions, of which 15 are for the general session (9 long papers, 6 short papers) and 5 are for the shared Task TermEval (3 long papers, 2 short papers). We retained 15 papers: 6 long papers for oral presentation (acceptance rate: 30%), of which 4 belong to the general session and 2 to TermEval, and 9 papers for poster presentation (4 long papers and 5 short papers), of which 7 belong to the general session (4 long papers and 3 short papers) and 2 to TermEval (2 short papers).", | |
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| "section": "Introduction", | |
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| "text": "The 6 long papers retained for oral presentations are the following: While these workshop proceedings have been published as planned, the workshop itself could not take place due to the current global pandemic. It is currently postponed indefinitely and any updates about this situation will be posted on the workshop website: https://sites.google.com/view/computerm2020. ", | |
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| "text": "Automatic Term Extraction from Newspaper Corpora: Making the Most of Specificity and Common Features Authors: Patrick Drouin, Jean-Beno\u00eet Morel and Marie-Claude L'Homme TermPortal: A Workbench for Automatic Term Extraction from Icelandic Texts Authors: Stein\u00fe\u00f3r Steingr\u00edmsson, \u00c1g\u00fasta \u00deorbergsd\u00f3ttir, Hjalti Danielsson and Gunnar Thor Ornolfsson Translating Knowledge Representations with Monolingual Word Embeddings: the Case of a Thesaurus on Corporate Non-Financial Reporting Authors: Mart\u00edn Quesada Zaragoza, Lianet Sep\u00falveda Torres and", | |
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| "content": "<table><tr><td>J\u00e9r\u00f4me Basdevant</td></tr><tr><td>Which Dependency Parser to Use for Distributional Semantics in a Specialized Domain? Authors:</td></tr><tr><td>Pauline Brunet, Olivier Ferret and Ludovic Tanguy</td></tr><tr><td>TermEval 2020: Shared Task on Automatic Term Extraction Using the Annotated Corpora for Term</td></tr><tr><td>Extraction Research (ACTER) Dataset Authors: Ayla Rigouts Terryn, Veronique Hoste, Patrick Drouin</td></tr><tr><td>and Els Lefever</td></tr><tr><td>TermEval 2020: TALN-LS2N System for Automatic Term Extraction Authors: Amir Hazem, M\u00e9rieme</td></tr><tr><td>Bouhandi, Florian Boudin and Beatrice Daille</td></tr></table>", | |
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